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STAT 497 LECTURE NOTES 6

STAT 497 LECTURE NOTES 6. SEASONAL TIME SERIES MODELS. SEASONAL TIME SERIES. A time series repeats itself after a regular period of time.

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STAT 497 LECTURE NOTES 6

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  1. STAT 497LECTURE NOTES 6 SEASONAL TIME SERIES MODELS

  2. SEASONAL TIME SERIES • A time series repeats itself after a regular period of time. • “Business cycle" plays an important role in economics. In time series analysis, business cycle is typically represented by a seasonal (or periodic) model. • A smallest time period for this repetitive phenomenon is called a seasonal period, s.

  3. SEASONAL TIME SERIES

  4. SEASONAL TIME SERIES Seasonality Deterministic Stochastic SARIMA • Seasonal means (dummies) + linear time trend • Sums of cosine curves at various frequencies + linear time trend

  5. SEASONAL TIME SERIES • For deterministic function f(.), we say that f(.) is periodic with a periodicity s if • A typical example of a deterministic periodic function is a trigonometric series, e.g. sin() = sin(+2k) or cos() = cos(+2k). • The trigonometric series are often used in econometrics to model time series with strong seasonality. [In some cases, seasonal dummy variables are used.]

  6. SEASONAL TIME SERIES • For stochastic process Yt, we say that it is a seasonal (or periodic) time series with periodicity s if Ytand Yt+kshave the same distribution. • For instance, the series of monthly sales of a department store in the U.S. tends to peak at December and to be periodic with a period 12. OR quarterly ice cream sales is seasonal with period 4. • In what follows, we shall use s to denote periodicity of a seasonal time series. Often s = 4 and 12 are used for quarterly and monthly time series, respectively.

  7. MODELLING SEASONALITY BY SEASONAL DUMMIES • One approach to model seasonality is regression on seasonal dummies. It is a simple application of dummy variables defined to reflect movement across the “seasons” of the year. • For quarterly data, s = 4, • For monthly data, s = 12, • For weekly data, s=52. • For daily data, s=365.25.

  8. SEASONAL DUMMY • Then we construct s seasonal dummy variables to indicate the season. So, if we have quarterly data and assuming the first observation we have is in the first quarter, we create: D1= (1,0,0,0,1,0,0,0,1,0,0,0,...) D2 = (0,1,0,0,0,1,0,0,0,1,0,0,…) D3 = (0,0,1,0,0,0,1,0,0,0,1,0,...) D4 =(0,0,0,1,0,0,0,1,0,0,0,1,...)

  9. SEASONAL DUMMY • D1 indicates whether we are in the first quarter (i.e., it takes on the value 1 in the first 1 quarter and 0 otherwise), • D2indicates whether we are in the second quarter, • D3indicates whether we are in the third quarter, • D4 indicates whether we are in the fourth quarter.

  10. SEASONAL DUMMY • The pure seasonal dummy model is given by: • This is simply a regression on an intercept in which we allow for a different intercept in each season. • These different intercepts are called the seasonal factors and reflect the seasonal pattern over the year.

  11. SEASONAL DUMMY • If we have s seasons, an alternative is to include just s-1 seasonal dummies and an intercept. In this case: (i) the constant term is the intercept for the omitted season; and (ii) the coefficients on the seasonal dummies indicate the seasonal increase/decrease relative to the omitted season. • Never include s seasonal dummies and an intercept. This will cause a serious problem.

  12. SEASONAL DUMMY AND LINEAR TIME TREND • If a variable Y exhibits both trend and seasonality, we can combine the trend model with the seasonal model and obtain: • Note that since we have used s seasonal dummies, we have dropped the intercept term from the linear trend part of the model.

  13. EXAMPLE > jj=read.table('c:/jj.dat', header=FALSE) > jj = ts(jj, start=1960, frequency=4) > time(jj) Qtr1 Qtr2 Qtr3 Qtr4 1960 1960.00 1960.25 1960.50 1960.75 1961 1961.00 1961.25 1961.50 1961.75 1962 1962.00 1962.25 1962.50 1962.75 1963 1963.00 1963.25 1963.50 1963.75 1964 1964.00 1964.25 1964.50 1964.75 1965 1965.00 1965.25 1965.50 1965.75 1966 1966.00 1966.25 1966.50 1966.75 1967 1967.00 1967.25 1967.50 1967.75 1968 1968.00 1968.25 1968.50 1968.75 1969 1969.00 1969.25 1969.50 1969.75 1970 1970.00 1970.25 1970.50 1970.75 1971 1971.00 1971.25 1971.50 1971.75 1972 1972.00 1972.25 1972.50 1972.75 1973 1973.00 1973.25 1973.50 1973.75 1974 1974.00 1974.25 1974.50 1974.75 1975 1975.00 1975.25 1975.50 1975.75 1976 1976.00 1976.25 1976.50 1976.75 1977 1977.00 1977.25 1977.50 1977.75 1978 1978.00 1978.25 1978.50 1978.75 1979 1979.00 1979.25 1979.50 1979.75 1980 1980.00 1980.25 1980.50 1980.75

  14. EXAMPLE Load FitAR. Write the time series as vector. Look at the Box-Cox results > jj.ts=as.vector(jj) > BoxCox(jj.ts) Use either 0.041-th power of the series or do ln transformations since the 0.041 is very close to 0.

  15. EXAMPLE > Q = factor(rep(1:4,21)) # make (Q)uarter factors [that's repeat 1,2,3,4, 21 times] > trend = time(jj)-1970 # not necessary to "center" time, but the results look nicer > reg = lm(log(jj)~0+trend+Q, na.action=NULL) # run the regression without an intercept > #-- the na.action statement is to retain time series attributes > summary(reg) Call: lm(formula = log(jj) ~ 0 + trend + Q, na.action = NULL) Residuals: Min 1Q Median 3Q Max -0.29318 -0.09062 -0.01180 0.08460 0.27644 Coefficients: Estimate Std. Error t value Pr(>|t|) trend 0.167172 0.002259 74.00 <2e-16 *** Q1 1.052793 0.027359 38.48 <2e-16 *** Q2 1.080916 0.027365 39.50 <2e-16 *** Q3 1.151024 0.027383 42.03 <2e-16 *** Q4 0.882266 0.027412 32.19 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1254 on 79 degrees of freedom Multiple R-squared: 0.9935, Adjusted R-squared: 0.9931 F-statistic: 2407 on 5 and 79 DF, p-value: < 2.2e-16

  16. EXAMPLE > plot(log(jj), type="o") # the data in black with little dots > lines(fitted(reg), col=2) # the fitted values in bloody red - or use lines(reg$fitted, col=2)

  17. EXAMPLE Plot of the residuals and the ACF of the residuals:

  18. FORECASTING SEASONAL SERIES • The full model is given by: • So, at time t = n+h, we have: • Note that to construct this forecast we have set an+hto its unconditional expectation of zero.

  19. FORECASTING SEASONAL SERIES • To make this point forecast operation we replace the unknown population parameters with OLS point estimates: • Finally, forecasts are formed.

  20. SEASONALITY • Seasonality can reflect other types calendar effects. The “standard” seasonality model with “seasonal dummies” is one type of calendar effect. Two other types of seasonality are holiday variation and trading-day variation. • Holiday variation refers to the fact the dates of some holidays change over time. Bairamis an important example, and we may want to include in a model with monthly data an “Bairam dummy” which equals 1 if the month contains Bairamand 0 otherwise.

  21. SEASONALITY • Likewise, trading-day variation refers to the fact that different months contain numbers of trading or business days. In a model of monthly retail sales, it would certainly seem to matter if there were, for example, 28, 29, 30, or 31 trading days in the month. To account for this we could include a trading-day variable which measures the number of trading days in the month. • We will not focus on holiday and trading-day variation effects, even though they are important in the analysis of many time series.

  22. PURE SEASONAL TIME SERIES • SARIMA(P,D,Q)s where 0 is constant,

  23. SARIMA(0,0,1)12=SMA(1)12 • This is a simple seasonal MA model. • Invertibility: ||< 1. • E(Yt) = 0.

  24. SARIMA(1,0,0)12 • This is a simple seasonal AR model. • Stationarity: ||<1. When  = 1, the series is non-stationary. To test for a unit root, consider seasonal unit root tests.

  25. MULTIPLICATIVE SEASONAL TIME SERIES • A special, parsimonious class of seasonal time series models that is commonly used in practice is the multiplicative seasonal model ARIMA(p, d, q)(P,D,Q)s. where all zeros of (B); (Bs); (B) and (Bs) lie outside the unit circle. Of course, there are no common factors between (B)(Bs) and(B)(Bs).

  26. MULTIPLICATIVE SEASONAL TIME SERIES

  27. MULTIPLICATIVE SEASONAL TIME SERIES

  28. MULTIPLICATIVE SEASONAL TIME SERIES

  29. MULTIPLICATIVE SEASONAL TIME SERIES

  30. MULTIPLICATIVE SEASONAL TIME SERIES

  31. Monthly Carbon Dioxide Levels at Alert, NWT, Canada

  32. AIRLINE MODEL • SARIMA(0,1,1)(0,1,1)12 where ||<1 and ||<1. • This model is the most used seasonal model in practice. It was proposed by Box and Jenkins (1976) for modeling the well-known monthly series of airline passengers.

  33. AIRLINE MODEL • Let Wt = (1  B)(1  B12)Yt, where (1  B) and (1  B12) are usually referred to as the “regular" and “seasonal" difference, respectively.

  34. AIRLINE MODEL

  35. SEASONAL UNIT ROOTS • Seasonal unit roots and testing for seasonal integration is discussed in Charemza and Deadman (1997, 105-9) and Pfaff (2008). • The main advantage of seasonal unit root tests is where you need to make use of data that cannot be seasonally adjusted or even as a pretest before seasonal adjustment.

  36. SEASONAL UNIT ROOTS • If a series has seasonal unit roots, then standard ADF test statistic do not have the same distribution as for non-seasonal series. Furthermore, seasonally adjusting series which contain seasonal unit roots can alias the seasonal roots to the zero frequency, so there is a number of reasons why economists are interested in seasonal unit roots. • Hylleberg, S., Engle, R.F., Granger, C. W. J., and Yoo, B. S., Seasonal integration and cointegration,(1990), Journal of Econometrics, 44: pages 215{238.

  37. THE DICKEY-HASZA-FULLER TEST

  38. DHF TEST • After the OLS estimation, the test statistics is obtained as • Again, the asymptotic distribution of this test statistics is a non-standard distribution. The critical values were obtained by Monte-Carlo simulation for different sample sizes and seasonal periods.

  39. DHF TEST • The problem of the DHF test is that, under the null hypothesis, one has exactly s unit roots. Under the alternative, one has no unit root. This is very restrictive, as some people may wish to test for specific seasonal or non-seasonal unit roots. The HEGY test by Hylleberg, Granger, Engle, Yoo can do this. Therefore, it is the most customary test.

  40. SEASONAL UNIT ROOTS • The HEGY test for seasonal integration is conducted by estimating the following regression (special case for quarterly data): where Qjt is a seasonal dummy, and the Wit are given below.

  41. HEGY TEST • After OLS estimation, tests are conducted for π1 = 0, for π2 = 0 and a joint test of the hypothesis π3 = π4 = 0. • The HEGY test is a joint test for LR (or zero frequency) unit roots and seasonal unit roots. If none of the πiare equal to zero, then the series is stationary (both at seasonal and nonseasonal frequencies).

  42. HEGY TEST • Interpretation of the different πiis as follows: 1. If π1 < 0, then there is no long-run (nonseasonal) unit root. π1 is on W1t = S(B)Ytwhich has had all of the seasonal roots removed. 2. If π2 < 0, then there is no semi-annual unit root. 3. If π3and π4 < 0, then there is no unit root in the annual cycle.

  43. HEGY TEST • Just as in the ADF tests, it is important to ensure that the residuals from estimating the HEGY equation are white noise. Thus, in testing for seasonal unit roots, it is important to follow the sequential procedures detailed above. • Again, begin by testing for the appropriate lag length for the dependent variable (to ensure serially uncorrelated residuals), and then test whether deterministic components belong in the model.

  44. HEGY TEST • The presence of seasonal unit roots at some frequency and not at other frequencies can lead to problems of interpretation. The presence of a seasonal unit root at a certain frequency implies that there is no deterministic cycle at that frequency but a stochastic cycle. • The power of unit root tests is low, that is, it is not easy to distinguish between genuine unit roots and near-unit roots. The literature suggests that this might not be too large a problem, as erroneously imposing a unit root seems better than not imposing it when one should.

  45. OSBORN-CHUI-SMITH-BIRCHENHALL (1988) TEST • Osborn, Chui, Smith and Birchenhall [1988] test is the modification of Dickey, Hasza and Fuller [1984]. • In R, seasonal unit root tests are implemented in the CRAN-package forecast package. • Osborn et al. [1988] suggested replacing Δszt with Δsyt as the dependent variable.

  46. OSBORN-CHUI-SMITH-BIRCHENHALL (1988) TEST • Incidentally, if h = 0, this is equivalent with an ADF regression for the seasonal differences; i.e., • The lag orders k and h should be determined similarly to the procedures proposed for the ADF test.

  47. OSBORN-CHUI-SMITH-BIRCHENHALL (1988) TEST • Furthermore, it should be noted that deterministic seasonal dummy variables can also be included in the test regression. The relevant critical values are provided in Osborn et al. [1988] and are dependent on the inclusion of such deterministic dummy variables and whether the data have been demeaned at the seasonal frequency.

  48. OSBORN-CHUI-SMITH-BIRCHENHALL (1988) TEST • If the null hypothesis of the existence of a seasonal unit root is rejected for a large enough absolute t ratio, then one might conclude that stochastic seasonality is not present or that stochastic seasonality, which can be removed by using s-differences, does not exist. On the other hand, if the null hypothesis cannot be rejected, it is common practice to consider the order of non-seasonal differencing required to achieve stationarity instead of considering higher orders of seasonal differencing.

  49. Number of differences required for a stationary series (ndiffs) • ndiffs {forecast} ndiffs uses a unit root test to determine the number of differences required for time series x to be made stationary. If test="kpss", the KPSS test is used with the null hypothesis that x has a stationary root against a unit-root alternative. Then the test returns the least number of differences required to pass the test at the level alpha. If test="adf", the Augmented Dickey-Fuller test is used and if test="pp" the Phillips-Perron test is used. In both of these cases, the null hypothesis is that x has a unit root against a stationary root alternative. Then the test returns the least number of differences required to fail the test at the level alpha.

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